Transient simultaneous heat and mass transfer model to estimate drying time in a wetted fur of a cow. (July 2020) - Chen, Elizabeth; Narayanan, Vinod; Pistochini, Theresa; Rasouli, Erfan
Abstract : To reduce heat stress that contributes to complications in milk production and breeding, dairy cows are typically cooled by intermittent water sprays coupled with fan-induced air flow. Industry currently utilises fixed duration on and off-cycles; however, water efficiency may be improved by matching the sprinkler off-time to the cow drying time that corresponds to current outdoor environmental conditions. Electricity use can also be reduced by varying fan speeds to achieve the required heat rejection rate for a given set of conditions. To achieve savings in water and electricity, a transient, one-dimensional simultaneous heat and mass transfer model of evaporation within the wetted fur layer of a dairy cows was developed to estimate drying time and heat rejection rate based on ambient conditions. Water vapour concentration, liquid water content, and temperature within the fur layer were used to estimate a global drying time for fur. Model results was compared against a steady-state model in literature and the predicted net heat load is within 15% with a mean average error of 6.0%. Parametric analyses were performed to estimate drying time as a function of outdoor air temperature, air speed, humidity, and mean radiant temperature. Simulation results were used to develop a correlation for use in a control algorithm to predict the fan speed and sprinkler operation frequency needed to meet specified cooling load thresholds given outdoor conditions. Estimated savings demonstrated that the model-based controller could reduce annual electricity and water consumption by 20% and 40%, respectively. Graphical abstract: Image 1 Highlights: Transient one-dimensional model for prediction of drying time from wetted cow fur. Drying variation with outdoor temperature, humidity, air speed and roof temperature. Predictive controller correlation based on regression of model simulation runs. Controller shown to lower annual electricity by 20% and water consumption by 40%.